In ESPEI, each data point is assumed to have a residual that follows a Gaussian distribution centered at zero, μ=0, with some user-specifiable standard deviation, σ:
for different types of contributions, i. Each type of data has a default standard deviation, σ_i, which are given in the table below for the each type of data currently available. Users may provide weights, w_i, which are the product of the "weight" key in each dataset and the value for the data type in the mcmc.data_weights dictionary from the ESPEI input. The default dataset and data weights are unity, so if no weights are provided, σ_i is the standard deviation.
By controlling the weights, the standard deviation each data point can be set.
| Likelihood contribution |
σ_i |
| ZPF (expressed as driving forces) |
1000 J/mol |
| HM |
500 J/mol |
| SM |
0.2 J/mol-K |
| CPM |
0.2 J/mol-K |
| ACR (activity, expressed as chemical potentials) |
500 J/mol |
In ESPEI, each data point is assumed to have a residual that follows a Gaussian distribution centered at zero, μ=0, with some user-specifiable standard deviation, σ:
for different types of contributions, i. Each type of data has a default standard deviation, σ_i, which are given in the table below for the each type of data currently available. Users may provide weights, w_i, which are the product of the
"weight"key in each dataset and the value for the data type in themcmc.data_weightsdictionary from the ESPEI input. The default dataset and data weights are unity, so if no weights are provided, σ_i is the standard deviation.By controlling the weights, the standard deviation each data point can be set.